We estimate the spatial distribution of heterogeneous physical parameters involved in the formation of magnetic domain patterns of polycrystalline thin films by using convolutional neural networks. We propose a method to obtain a spatial map of physical parameters by estimating the parameters from patterns within a small subregion window of the full magnetic domain and subsequently shifting this window. To enhance the accuracy of parameter estimation in such subregions, we employ large-scale models utilized for natural image classification and exploit the benefits of pretraining. Using a model with high estimation accuracy on these subregions, we conduct inference on simulation data featuring spatially varying parameters and demonstrate the capability to detect such parameter variations.
@article{arxiv.2305.14764,
title = {Detection of Non-uniformity in Parameters for Magnetic Domain Pattern Generation by Machine Learning},
author = {Naoya Mamada and Masaichiro Mizumaki and Ichiro Akai and Toru Aonishi},
journal= {arXiv preprint arXiv:2305.14764},
year = {2023}
}